
import copy
from collections import OrderedDict
from typing import Dict, Mapping

from packaging import version

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
from ..auto import CONFIG_MAPPING


__lowerCAmelCase     :	List[str]       			=  logging.get_logger(__name__)

__lowerCAmelCase     :	Tuple       			=  {
    'facebook/detr-resnet-50': 'https://huggingface.co/facebook/detr-resnet-50/resolve/main/config.json',
    # See all DETR models at https://huggingface.co/models?filter=detr
}




class 			UpperCAmelCase_     (					_A	):


  '''simple docstring'''







  a__       			=						"""detr"""
  a__       			=						["""past_key_values"""]
  a__       			=						{
      """hidden_size""": """d_model""",
      """num_attention_heads""": """encoder_attention_heads""",
  }
  def __init__(					self     :					Union[str, Any]     ,				UpperCamelCase__     :					Dict=True     ,				UpperCamelCase__     :					int=None     ,				UpperCamelCase__     :					int=3     ,				UpperCamelCase__     :					Dict=100     ,				UpperCamelCase__     :					Union[str, Any]=6     ,				UpperCamelCase__     :					str=2048     ,				UpperCamelCase__     :					Optional[int]=8     ,				UpperCamelCase__     :					int=6     ,				UpperCamelCase__     :					Dict=2048     ,				UpperCamelCase__     :					Tuple=8     ,				UpperCamelCase__     :					Optional[int]=0.0     ,				UpperCamelCase__     :					Optional[Any]=0.0     ,				UpperCamelCase__     :					List[str]=True     ,				UpperCamelCase__     :					List[str]="relu"     ,				UpperCamelCase__     :					Tuple=256     ,				UpperCamelCase__     :					Any=0.1     ,				UpperCamelCase__     :					List[Any]=0.0     ,				UpperCamelCase__     :					Any=0.0     ,				UpperCamelCase__     :					Optional[int]=0.02     ,				UpperCamelCase__     :					Optional[int]=1.0     ,				UpperCamelCase__     :					int=False     ,				UpperCamelCase__     :					Optional[int]="sine"     ,				UpperCamelCase__     :					Dict="resnet50"     ,				UpperCamelCase__     :					Any=True     ,				UpperCamelCase__     :					List[Any]=False     ,				UpperCamelCase__     :					int=1     ,				UpperCamelCase__     :					str=5     ,				UpperCamelCase__     :					Union[str, Any]=2     ,				UpperCamelCase__     :					Any=1     ,				UpperCamelCase__     :					Union[str, Any]=1     ,				UpperCamelCase__     :					Dict=5     ,				UpperCamelCase__     :					Optional[Any]=2     ,				UpperCamelCase__     :					Any=0.1     ,				**UpperCamelCase__     :					List[Any]     ,				)     ->      Union[str, Any]:







    """simple docstring"""






    if backbone_config is not None and use_timm_backbone:
      raise ValueError("""You can't specify both `backbone_config` and `use_timm_backbone`."""       )

    if not use_timm_backbone:
      if backbone_config is None:
        logger.info("""`backbone_config` is `None`. Initializing the config with the default `ResNet` backbone."""       )
        __magic_name__								=			CONFIG_MAPPING["""resnet"""](out_features=["""stage4"""]       )
      elif isinstance(UpperCamelCase__     ,				UpperCamelCase__       ):
        __magic_name__								=			backbone_config.get("""model_type"""       )
        __magic_name__								=			CONFIG_MAPPING[backbone_model_type]
        __magic_name__								=			config_class.from_dict(UpperCamelCase__       )
      # set timm attributes to None
      __magic_name__	,       __magic_name__	,       __magic_name__								=			None, None, None

    __magic_name__								=			use_timm_backbone
    __magic_name__								=			backbone_config
    __magic_name__								=			num_channels
    __magic_name__								=			num_queries
    __magic_name__								=			d_model
    __magic_name__								=			encoder_ffn_dim
    __magic_name__								=			encoder_layers
    __magic_name__								=			encoder_attention_heads
    __magic_name__								=			decoder_ffn_dim
    __magic_name__								=			decoder_layers
    __magic_name__								=			decoder_attention_heads
    __magic_name__								=			dropout
    __magic_name__								=			attention_dropout
    __magic_name__								=			activation_dropout
    __magic_name__								=			activation_function
    __magic_name__								=			init_std
    __magic_name__								=			init_xavier_std
    __magic_name__								=			encoder_layerdrop
    __magic_name__								=			decoder_layerdrop
    __magic_name__								=			encoder_layers
    __magic_name__								=			auxiliary_loss
    __magic_name__								=			position_embedding_type
    __magic_name__								=			backbone
    __magic_name__								=			use_pretrained_backbone
    __magic_name__								=			dilation
    # Hungarian matcher
    __magic_name__								=			class_cost
    __magic_name__								=			bbox_cost
    __magic_name__								=			giou_cost
    # Loss coefficients
    __magic_name__								=			mask_loss_coefficient
    __magic_name__								=			dice_loss_coefficient
    __magic_name__								=			bbox_loss_coefficient
    __magic_name__								=			giou_loss_coefficient
    __magic_name__								=			eos_coefficient
    super().__init__(is_encoder_decoder=UpperCamelCase__     ,				**UpperCamelCase__       )
  @property
  def 				_lowercase				(					self     :					List[Any]       )     ->      int:







    """simple docstring"""






    return self.encoder_attention_heads
  @property
  def 				_lowercase				(					self     :					str       )     ->      int:







    """simple docstring"""






    return self.d_model
  @classmethod
  def 				_lowercase				(					cls     :					Optional[Any]     ,				UpperCamelCase__     :					PretrainedConfig     ,				**UpperCamelCase__     :					Union[str, Any]       )     ->      Dict:







    """simple docstring"""






    return cls(backbone_config=UpperCamelCase__     ,				**UpperCamelCase__       )





  def 				_lowercase				(					self     :					Any       )     ->      Dict[str, any]:







    """simple docstring"""






    __magic_name__								=			copy.deepcopy(self.__dict__       )
    if output["backbone_config"] is not None:
      __magic_name__								=			self.backbone_config.to_dict()
    __magic_name__								=			self.__class__.model_type
    return output







class 			UpperCAmelCase_     (					_A	):


  '''simple docstring'''







  a__       			=						version.parse("""1.11"""	)
  @property
  def 				_lowercase				(					self     :					Any       )     ->      Mapping[str, Mapping[int, str]]:







    """simple docstring"""






    return OrderedDict(
        [
            ("""pixel_values""", {0: """batch""", 1: """num_channels""", 2: """height""", 3: """width"""}),
            ("""pixel_mask""", {0: """batch"""}),
        ]       )
  @property
  def 				_lowercase				(					self     :					List[str]       )     ->      float:







    """simple docstring"""






    return 1E-5





  @property
  def 				_lowercase				(					self     :					List[Any]       )     ->      int:







    """simple docstring"""






    return 12



from maths.is_square_free import is_square_free
from maths.prime_factors import prime_factors

def a__       (			A_   ):






  '''simple docstring'''





  __magic_name__								=			prime_factors(A_   )
  if is_square_free(A_   ):
    return -1 if len(A_   ) % 2 else 1
  return 0


if __name__ == "__main__":
    import doctest

    doctest.testmod()

